LlamaIndex supports a wide range of data formats to help developers integrate diverse data sources with large language models (LLMs). The framework is designed to handle structured, semi-structured, and unstructured data, making it adaptable for many use cases. Common formats include plain text files, CSV, JSON, PDFs, and HTML. For example, text files or Markdown documents can be loaded directly, while structured data like CSV or JSON can be parsed into a format that LLMs can process. This flexibility allows developers to work with data from spreadsheets, APIs, databases, or web pages without extensive preprocessing.
Beyond basic file types, LlamaIndex also integrates with databases and third-party services. It supports SQL databases (like PostgreSQL or SQLite) through query interfaces, enabling direct retrieval of structured data. For semi-structured data sources such as Notion, Slack, or Google Docs, LlamaIndex provides pre-built connectors or “readers” that simplify data ingestion. For instance, the NotionPageReader can extract text from Notion pages, while the SimpleWebPageReader fetches and processes HTML content from URLs. These tools reduce the effort required to unify data from different platforms, letting developers focus on structuring the data for LLM interactions.
Developers can also extend LlamaIndex to handle custom or niche formats. The framework’s modular design allows users to create custom data loaders or preprocessing pipelines. For example, if you need to process audio or image files, you could integrate speech-to-text or OCR libraries to convert these files into text before feeding them into LlamaIndex. Additionally, the framework supports parsing code repositories (like Python files) or specialized formats such as emails (via .eml files) using community-contributed or custom modules. This adaptability ensures that even less common data types can be incorporated into LLM-powered applications with minimal friction.